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Data from: Plant-pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes

Cite this dataset

Udy, Kristy; Reininghaus, Hannah; Scherber, Christoph; Tscharntke, Teja (2020). Data from: Plant-pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes [Dataset]. Dryad.


Urbanization affects pollinator diversity and plant-pollinator networks by changing resource availability locally and in the surrounding landscape. We experimentally established (N = 12) standardized plant communities in farmland, villages and cities to identify the relative role of local and landscape effects on plant-pollinator communities along this urbanization gradient. We found that the number of flower visits by solitary bees, but not bumblebees, were highest in cities and lowest in farmland, with villages being intermediate, whereas syrphid flies exhibited lowest numbers in cities. Villages supported the richest pollinator communities, as they appeared to benefit from both farmland and city communities. Plant-pollinator network metrics such as robustness, interaction evenness and interaction diversity decreased with increasing urbanization, although local plant richness increased towards urban areas. In conclusion, pollinator communities were most diverse and stable in farmland and village sites, despite the high plant richness in cities. The different composition of pollinator communities along the urbanization gradient suggests considering all three landscape types for conservation schemes.


Study sites

Insect observations were conducted on an urbanization gradient from farmland and villages to cities, including grassy field margins in pure farmland, and gardens (c. 800-1000 m²) in villages and cities. Farmland sites were at least 500 m from the nearest house. Village sites were close to the village edge and were surrounded by a 500 m buffer comprising approximately 50% urban and 50% farmland. City sites were at least 500 m from the city edge and were surrounded by a buffer of 100% urban area. The urbanization gradient was constructed in this way to test the influence of amount of farmland in the landscape and the level of urbanization. N=12 sites were used: four farmland sites (maximum distance 30 km from Göttingen), two villages (two gardens each: Dransfeld (51°50'06.01"N, 9°76'23.95"E) and Diemarden (51°48'72.82"N, 9°98'05.67"E) and two cities (two gardens each: Göttingen and Einbeck (51°49'13.29"N, 9°52'6.14"E), separated by a minimum of 500 m inside the city border).

Experimental plant plots

Experimental plant patches were established in April 2015 (size 80 x 80 cm) in each of the 12 sites. We standardized soil conditions by using a soil mix at all sites (mix from volcanic clay, peat, lime carbonate and NPK fertilizer; 180 mg/L N, 180 mg/L P; 260 mg/L K; 130 mg/L Mg and 100 mg/L of S with a pH of 5.9). Approximately 30 mL of NPK fertilizer was added when the seeds were planted, which contained equal parts N (8%) and P (8%). The numbers of plant seeds used were standardized to approximately 20 seeds per plant species and were evenly scattered over the soil. The plant species used were Phacelia tanacetifolia (Benth.) and Sinapis arvensis (L.). Plant patches were watered once a week with 10 L of water and fertilised once more after one month. The perennial garden plants Veronica spicata (L.) and Astilbe chinensis (Maxim.) were bought from a commercial supplier (Baumschule Jenssen, Göttingen) and transplanted to the experimental plots in June. This mixture of four plant species included plants with high quality pollen and nectar that are attractive to pollinators. The plant species covered a wide range of flower types, both open and tubular, and a mixture of colors: yellow, white and purple (Pritsch 2007). All plant species flowered simultaneously at the start of July for two weeks.


Insect observations

Insect observations were run in early July 2015 for 15-minute intervals at two different times of the day (total observation hours = 6): morning (10-11:30) and midday (12:45-14:30), these times were centered on midday (13:15), calculated as the midpoint between sunrise and sunset. Observations were run in early July as midsummer is when pollinator richness is high in urban and agriculture areas. Six sites were visited each day, three per time-period, and the order in which they were visited was randomized. Observations were conducted on a corner of each plant plot (50 x 50 cm) that included all plant species. We observed all insect pollinators that visited a flower, identified them to genus or species level and counted the number of visits (landing on a single flower equals one visit) for each insect until it left the plant plot. If it was not possible to identify an individual to species level, we identified it as accurately as possible (e.g. to genus level) and assigned a morphospecies to it. We also recorded which plant species each insect pollinator was observed on. Insect pollinators included: solitary bees (i.e. non-bumblebees), bumblebees, butterflies, syrphid flies, non-syrphid flies, wasps and honeybees. To assess the plant species richness neighboring the plant plots, we counted all plant species within a radius of 20 m that were flowering at that time.

Statistical analyses

We found no differences in pollinator richness and their abundance between morning and midday observations; thus, abundances were summed for every observation day. All analyses were performed using R (version 3.5.1; R Core Team 2018). The response variables (number of pollinator visits, plant richness and pollinator morphospecies richness) were modelled as functions of landscape type (a factor with three levels) and plant species richness (numeric). Plant species richness was influenced by landscape type and was always tested in separate models.

We used a series of generalized least squares (GLS) models and linear mixed-effects models (LME; all fitted using REML) to account for potential spatial non-independence in our data: a

GLS model without heterogeneity, a GLS model with spherical autocorrelation (longitude, latitude), and an LME model with random effects for every site (N=12). These models were ranked using AICc (Information Theoretic approach). For all variables, the models with the lowest AICc were simple GLS models without a correlation structure or random effect.

We tested which distribution fitted each response variable best, using the fitdistrplus package. In all cases, models with an untransformed response variable had lowest AICc values, except for plant richness and pollinator morphospecies richness for which a log-normal distribution had lowest AICc values. Corresponding negative binomial or poisson models did not fit the data adequately. Results were plotted using the effects package.

The proportional abundance of the seven different pollinator groups was tested using multinomial models against the explanatory variables. Bipartite networks (N=12) were created from the plant-pollinator interactions for each site and their structure analyzed with network level metrics using the bipartite package. The network level metrics used were: robustness, interaction evenness and Shannon diversity of interactions. Robustness is a measure of the stability of the network, specifically it is calculated by measuring the area below the extinction curve generated as a measure of the robustness of the network to the loss of species.


German Research Foundation, Award: RTG1644